1Using evolutionary functional-structural plant modelling to understand the effect of

2climate change on plant communities

1* 3Jorad de Vries

1 4 ETH Zürich, Institute for Integrative Biology, Zürich, Switzerland

5* Correspondence: [email protected]

6

7Abstract

8The “holy grail” of trait-based ecology is to predict the fitness of a species in a particular

9environment based on its functional traits, which has become all the more relevant in the light

10of global change. However, current ecological models are ill-equipped for this job: they rely

11on statistical methods and current observations rather than the mechanisms that determine

12how functional traits interact with the environment to determine plant fitness, meaning that

13they are unable to predict ecological responses to novel conditions. Here, I advocate the use

14of a 3D mechanistic modelling approach called functional-structural plant (FSP) modelling in

15combination with evolutionary modelling to explore climate change responses in natural

16plant communities. Gaining a mechanistic understanding of how trait-environment

17interactions drive natural selection in novel environments requires consideration of individual

18plants with multidimensional phenotypes in dynamic environments that include abiotic

19gradients and biotic interactions, and their combined effect on the different vital rates that

20determine plant fitness. Evolutionary FSP modelling explicitly simulates the trait-

21environment interactions that drive eco-evolutionary dynamics from individual to community

22scales and allows for efficient navigation of the large, complex and dynamic fitness

23landscapes that emerge from considering multidimensional plants in multidimensional

24environments. Using evolutionary FSP modelling as a tool to study climate change responses

25of plant communities can further our understanding of the mechanistic basis of these

26responses, and in particular, the role of local adaptation, phenotypic plasticity, and gene flow.

1 27Introduction

28The “holy grail” of trait-based ecology is to predict the fitness of a species in a particular

29environment based on its functional traits (Lavorel and Garnier 2002), which has become all

30the more relevant in the light of global change (Funk et al. 2017). The persistence of many

31plant species relies on their ability to either adapt to changing conditions in their current

32habitat range, or to track their climatic niche beyond their current range and into previously

33unoccupied habitats. Either way, these plant species will face an array of novel abiotic and

34biotic conditions that exert selection pressures not experienced within their current range

35(Franks et al. 2007, Franks and Weis 2008, Lustenhouwer et al. 2018). Predicting how plant

36populations and communities will respond to the novel conditions caused by climate change

37has thus far been challenging, because the roles of key eco-evolutionary mechanisms such as

38adaptive evolution, phenotypic plasticity and gene flow are still poorly understood (Anderson

39and Song 2020). Furthermore, current ecological models are ill-equipped to predict

40ecological responses to novel conditions due to their reliance on statistical methods and

41current observations (Pagel and Schurr 2012) rather than the mechanisms that determine how

42functional traits interact with the environment, thereby determining plant fitness (Williams

43and Jackson 2007, Angert et al. 2011, Alexander et al. 2016, Urban et al. 2016). This calls for

44the development of novel mechanistic modelling approaches designed to make predictions

45and formulate hypotheses on the adaptive value of functional traits and life-history strategies

46in a changing world (Urban et al. 2016). Here, I will advocate the utilisation of a 3D

47mechanistic modelling approach called functional-structural plant (FSP) modelling (Evers et

48al. 2018, Louarn and Song 2020), in combination with evolutionary modelling to explore

49climate change responses of natural plant communities. First, I will explain why

50understanding climate change responses of plant communities requires mechanistic

51modelling approaches. Second, I will introduce FSP modelling and discuss how FSP models

2 52can link individual plants to their (a)biotic environment to accurately simulate the trait-

53environment interactions that drive climate change responses of individual plants. Thirds, I

54will discuss how coupling FSP and evolutionary models allows scaling from individuals to

55communities through mechanistic simulation of demographic and evolutionary processes.

56Fourth, I will discuss how evolutionary FSP modelling can help explore the behaviour of

57complex systems with multidimensional plant phenotypes in multidimensional environments.

58Last, I will highlight the importance of considering the spatial and temporal dynamics of

59these multidimensional environments, their effects on selection, and the role of phenotypic

60plasticity.

61Understanding climate change responses requires mechanistic modelling approaches

62Climate change responses of plant communities are not dominated by any one trait or

63environmental factor, but rather, are the product of interactions between multiple traits,

64abiotic factors, biotic interactions, and demographic processes (McGill et al. 2006, Laughlin

65and Messier 2015). Plant communities are complex mixtures of different species that

66represent a range of functional strategies. In turn, each species within a community has

67functional trait variation, which affect the different vital rates that determine the fitness of

68that species (Laughlin et al. 2020). While functional traits are a great tool to describe

69variation in functional strategies on the species and community levels (Wright et al. 2004,

70Díaz et al. 2016), they have proven to be poor predictors of ecosystem functioning (van der

71Plas et al. 2020). This indicates that species level functional trait variation may fail to capture

72the granularity required to accurately link traits to vital rates, and that traits taken out of the

73context of the individual lack predictive power (Yang et al. 2018). This is highlighted by two

74observations: first, the fact that multiple functional strategies can coexist in a given

75environment challenges the idea that traits can predict ecosystem level responses in a detailed

76way (Adler et al. 2014). Second, functional trait variation on the species level is often the

3 77result of multiple populations that have adapted to their local environmental conditions,

78showing that functional traits variation needs to be considered in the context of the local

79environment. This population level variation is important to consider in the context of climate

80change, as it can either improve or impede a species’ ability to track their environmental

81niche or adapt to local environmental change (Atkins and Travis 2010, Anderson and Song

822020, Anderson and Wadgymar 2020). Observed patterns of local adaptation along

83environmental gradients suggest that populations at the species’ cold range edge are generally

84adapted to abiotic conditions, while populations at the warm range edge are generally adapted

85to biotic interactions (Griffith and Watson 2005, Hargreaves et al. 2014). These observations

86are in accordance with ecological theory, which suggests that the selection pressures exerted

87by abiotic conditions play a larger role in environments that are abiotically stressful, while

88biotic interactions play a larger role in more benign environments (Louthan et al. 2015,

89Briscoe Runquist et al. 2020). However, how abiotic and biotic selection pressures interact to

90shape locally adapted phenotypes is not clear-cut (Briscoe Runquist et al. 2020, Hargreaves et

91al. 2020), yet these interactions are key to understanding climate change impacts on plant

92communities (HilleRisLambers et al. 2013). Therefore, understanding how plant communities

93respond to the novel conditions resulting from climate change requires a focus on the

94mechanisms that link the functional traits to fitness on the level of individual plants through

95interactions with their abiotic and biotic environment.

96FSP modelling: linking the plant to their (a)biotic environment

97Perhaps the most important mechanism that links plant form and function to plant fitness in

98relation to its local (a)biotic environment is the acquisition of, and competition for resources

99such as light, water and nutrients, which shape plant communities through niche

100differentiation and competitive exclusion (Kunstler et al. 2016, Levine et al. 2017, Adler et

101al. 2018). Plant community structure is expected to change as a result of climate change,

4 102either through changes in the interactions between competitors that currently co-exist, or

103through the introduction of novel competitors. This leads to major changes in the identity and

104strength of competitive interactions, as well as the environmental context in which these

105interactions occur (Alexander et al. 2015). Predicting how these changes in competitive

106interactions may affect future plant communities requires modelling approaches to simulate

107the mechanisms of resource acquisition and competition, and their effects on demographic

108processes (Alexander et al. 2016). Competitive interactions are strong drivers of selection,

109because traits that favour resource acquisition may allow pre-emptive access to that resource

110(i.e. a tall plant shades a short plant but not vice versa, Falster and Westoby 2003), and may

111lead to competitive asymmetry (i.e. a tall plant acquires a disporportionate share of resources

112compared to a small plant, Weiner 1990, McNickle and Dybzinski 2013). Integrating these

113mechanisms is key to understanding climate change responses of plant communities, but

114requires both a high spatial resolution that captures plant architecture and resource

115heterogeneity on the sub-individual-level, as well as a community-level perspective that

116captures relative trait values of individually distinct plants.

117 To this end, I propose the use of FSP modelling, which is a mechanistic modelling

118approach that simulates the performance of individual plants through an explicit

119representation of plant structure in a 3D environment in combination with functional

120responses to that environment. FSP modelling is a versatile toolbox that can integrate scales

121ranging from gene to community levels, represent a wide range of systems, and answer a

122wide variety of questions (Evers et al. 2018, Louarn and Song 2020). FSP modelling is an

123excellent tool to simulate climate change responses of plant communities because of their

124ability to simulate a high level of spatial detail, which allows simulation of the mechanisms

125that underly interactions between traits and the (a)biotic environment and from which

126relationships between traits and fitness emerge. FSP models have widely adopted the carbon

5 127economy as the basis to simulate the link between plant form and function and plant fitness in

128relation to the (a)biotic environment (Sterck and Schieving 2007, Evers et al. 2010, Gauthier

129et al. 2020). The carbon economy encompasses the assimilation of carbon through

130photosynthesis, the allocation of these assimilates to drive plant growth and development, and

131the loss of these assimilates through respiration, tissue death or exudation. The carbon

132economy is strongly dependent on the abiotic environment, as both photosynthesis and plant

133development are temperature dependent processes, and because photosynthesis requires the

134acquisition of light, CO2, water and nutrients. Implementation of the carbon economy allows

135FSP models to explore how changes in key climatic variables such as increased temperatures,

136elevated CO2 levels, and altered water availability affect plant growth and development. To

137describe plant form and function, FSP models may take a trait-based approach that integrates

138a large variety of morphological, physiological and phenological plant traits, such as leaf

139shape (Schmidt and Kahlen 2018), plant height (Renton et al. 2005), leaf insertion angle (de

140Wit et al. 2012), root insertion angle (Postma et al. 2014), defence expression (de Vries et al.

1412019), and flowering time (de Vries et al. 2018), among many others. FSP modelling is then

142able to link these traits to the abiotic environment, including nutrients (Dunbabin et al. 2004),

143water (Braghiere et al. 2020), light (Hitz et al. 2019), and temperature (Chen et al. 2014). FSP

144models can capture the heterogeneity in resource availability, plant morphology, and plant

145functional responses in a high level of spatial detail, thereby accurately simulating how these

146climatic variables drive plant growth and development, and mediate competitive interactions.

147Thus far, FSP models have mostly considered the acquisition and competition for different

148above- and belowground resources in isolation (Postma and Lynch 2012, Evers and Bastiaans

1492016, Bongers et al. 2018, de Vries et al. 2018), but recent developments have seen the

150integration of the two resource systems in whole-plant models that simulate both shoot and

151root architectures (Drouet and Pagès 2007, Louarn and Faverjon 2018, Zhou et al. 2020, de

6 152Vries et al. 2021). Such a whole-plant approach is imperative to simulating plant community

153dynamics, because competition for multiple resources drives niche differentiation and shapes

154different functional strategies (Kunstler et al. 2016, Levine et al. 2017, Adler et al. 2018).

155This highlights how the carbon economy is not only the foundational mechanism that links

156the plant to their abiotic environment, but also the link through which interactions between

157the plant and their biotic environment play out.

158 FSP modelling is an excellent tool to simulate plants in a broad ecological context,

159and has been used to study a myriad of biotic interaction such as plant-plant (Bongers et al.

1602014, Evers and Bastiaans 2016, Faverjon et al. 2019), plant-herbivore (de Vries et al. 2018),

161plant-pathogen (Robert et al. 2008, Garin et al. 2014, Streit et al. 2017), or plant-mycorrhizal

162interactions (Schnepf et al. 2016, de Vries et al. 2021). However, these biotic interactions are

163mostly simulated in isolation, and models that simulate interactions between biotic agents

164have only seen recent development (Douma et al. 2019, de Vries et al. 2021). These

165interactions between biotic agents are known play an important role in shaping climate

166change responses of plant communities (HilleRisLambers et al. 2013, Post 2013), which calls

167for the integration of multiple biotic interactions into FSP models designed to simulate

168climate change responses of plant communities. Similarly, FSP models that focus on natural

169systems are often used to simulate single plant species rather than diverse mixed-species

170communities, which have received only recent attention (Faverjon et al. 2019, Bongers 2020).

171Simulation of mixed-species plant communities is required to link variation in physiological,

172phenological and morphological traits to plant community structure (Zakharova et al. 2019),

173which is a strong driver of the competitive interactions among plants (Alexander et al. 2016,

174Adler et al. 2018), as well as interactions between plants and pollinators (Sargent and Ackerly

1752008), soil microbes (Hodge and Fitter 2013), pests (Agrawal et al. 2006), and pathogens

7 176(Mordecai 2011). This calls for an increased focus on the development of FSP modelling

177tools designed to simulate mixed-species plant communities going forward.

178Coupling FSP and evolutionary models

179Understanding the full scope of the eco-evolutionary dynamics that drive climate change

180responses of plant communities requires explicit consideration of population level processes,

181both genetic and demographic, that drive evolution through selection, genetic drift and gene

182flow (Lowe et al. 2017). In particular, gene flow between populations is known to play a

183complex evolutionary role as it can either promote or constrain adaptation (Garant et al.

1842007). Low amounts of gene flow ensure that beneficial alleles can spread across populations

185to maintain adaptive genetic variation (Slatkin 1987, Rieseberg and Burke 2001, Tallmon et

186al. 2004), while large amounts of gene flow can homogenise populations and work against

187the diversifying forces of mutation, genetic drift and directional selection that drive local

188adaptation (Haldane 1930, García‐ Ramos and Kirkpatrick 1997, but see Fitzpatrick et al.

1892015). Because FSP modelling is a trait- and individual-based modelling approach, it can

190accommodate intraspecific trait variation (Zakharova et al. 2019), which is the basis of these

191eco-evolutionary processes and is therefore key to predict community responses to

192environmental change (Bolnick et al. 2011). To couple FSP and evolutionary models, one or

193more model parameters have to be made subject to selection, gene flow and genetic drift.

194This requires a definition of fitness to drives selection for these parameters, the incorporation

195of mechanisms that link these parameters to fitness, and for these parameters to be heritable

196(Fig. 1). These heritable parameters will most commonly constitute a selection of functional

197traits (de Vries et al. 2020), but can also constitute genes or even the shape of a plastic

198response (Bongers et al. 2019), depending on the model. Similarly, plant fitness will most

199commonly be defined as reproduction, but, depending on the model, fitness can include male

200and female fecundity and survival. The combination of FSP and evolutionary modelling

8 201allows for the mechanistic simulation of demographic and evolutionary processes from which

202contrasting functional strategies along multiple environmental gradients emerge (Bornhofen

203et al. 2011, de Vries et al. 2020). Additionally, this mechanistic approach can simulate co-

204evolution between species and selection-driven changes in species interactions as emergent

205model behaviour, because the underlying mechanisms simulated by the FSP model are driven

206not only by absolute trait values, but also by trait values relative to those of neighbouring

207plants. Integrating physiological, demographic and evolutionary processes in the same model

208requires balancing the high temporal and spatial resolution required for physiological

209processes with the large temporal and spatial extend required for demographic and

210evolutionary processes (Fig. 1, Oddou-Muratorio et al. 2020, Zhang and DeAngelis 2020).

211This balancing of resolution and extent is also prevalent when comparing the simulation of

212small, short-lived plant species that allow for more spatial and temporal detail, to simulation

213of trees, which require sacrificing spatial and temporal detail to balance the increased

214computational demand inherent to simulating large, long-lived plant species. To deal with

215this constraint, evolutionary FSP models can represent a community of plants that stretches

216across an environmental gradient as a series of small, local communities consisting of tens to

217hundreds of individuals, depending on their size, to infer the selection pressure at discrete

218points along the environmental gradient (Bongers et al. 2019, de Vries et al. 2020).

219Subsequently, to simulate the larger spatial extent required for evolutionary processes such as

220gene flow, the evolutionary FSP models can be designed to simulate several of these small,

221local communities in parallel, and connect them through a sub-model of pollen and seed

222dispersal (Colbach 2009). By striking this balance between the integration of detailed

223physiological processes and large scale demographic and evolutionary processes,

224evolutionary FSP modelling offers a unique opportunity to unravel how climate change

225affects plant communities.

9 226Simulating selection of multidimensional phenotypes in multidimensional environments

227Plants exist in complex environments where multiple (a)biotic drivers of selection interact

228with multiple plant functional traits to determine the different vital rates that make up plants

229fitness; growth, reproduction and survival (Fig. 1; McGill et al. 2006, Laughlin and Messier

2302015, Laughlin et al. 2020). Describing functional differences between species therefore

231requires consideration of multiple trait axes that differentiate between species in multiple

232ecological dimensions (Laughlin 2014, Kraft et al. 2015), and of the interactions between

233these traits axes that give rise to trade-offs, and define functional strategies (Wright et al.

2342004, Sterck et al. 2011, Díaz et al. 2016, Züst and Agrawal 2017). However, integrating this

235multidimensionality in both the plant phenotypes and their environment leads to an

236exponentially increasing computational demand. This computational demand can be

237drastically reduced by using an evolutionary algorithm to explore the behaviour of complex

238FSP models that integrate multidimensional phenotypes and environments. This is achieved

239by the evolutionary model only simulating an evolutionary trajectory from a set of initial trait

240values to an optimal combination of trait values, rather than using a full factorial simulation

241design to fully explore the fitness landscape (a conceptual representation of trait-fitness

242relationships as a surface with peaks and valleys that is described by one or more trait axes,

243see Gavrilets 2010, Svensson and Calsbeek 2012, De Visser and Krug 2014). The

244computational benefits of this approach can be exemplified by the work of Renton and Poot

245(2014), who developed an evolutionary FSP model that simulated single root systems

246searching for bedrock cracks that would provide access to ground water and ensure their

247survival in the dry Australian summer. The model was used to simulate selection in a large

16 248and complex fitness landscape consisting of 16 trait axes, which would require 10

249simulations to explore using a full factorial simulation design and ten values for each trait.

250However, because an evolutionary algorithm was used to traverse evolutionary trajectories

10 251across the fitness landscape (rather than using a full factorial simulation design), these

16 252potential 10 simulations were reduced to a more manageable 800 000 simulations (2000

253generations * 100 plants * four runs). The work by Renton and Poot (2014) also demonstrates

254that a large fitness landscape may have multiple local optima that will be missed when only

255traversing a single evolutionary trajectory through the fitness landscape. To identify the

256presence and locations of these local optima requires multiple, preferably randomly

257generated, initial conditions, both through an initial population of plants with randomly

258generated genotypes and through replication of the evolutionary runs. Although the fitness

259landscape investigated by Renton and Poot (2014) was both large and complex (i.e.

260consisting of many interacting trait axes), both the environment and the drivers of selection

261did not change between generations and the plants were simulated in isolation rather than in

262competition with other plants. This resulted in a static fitness landscape that did not change

263over time, contrasting the dynamic fitness landscapes that shape plant communities. Recent

264years have seen the development of three other evolutionary FSP models that simulated

265dynamic fitness landscapes driven by resource competition between plants (Yoshinaka et al.

2662018, Bongers et al. 2019, de Vries et al. 2020), marking the first steps towards simulating

267selection in a dynamic environment.

268Phenotypic plasticity and selection in a dynamic environment

269Plants live in dynamic environments where both abiotic conditions and biotic interactions

270vary over temporal and spatial scales, and climate change is expected to increase the strength

271and frequency of environmental variation, and in particular the frequency of disturbances

272such as heat waves, droughts, pathogens or insect pests (Seidl et al. 2017). Plant populations

273can be seen as the combined results of selection pressures exerted by the environment over

274large temporal and spatial scales, as these environmental dynamics determine the dynamics

275of the fitness landscape (MacColl 2011). Therefore, a phenotype observed in a natural system

11 276is not necessarily optimised for the local environment in which it is observed, but rather

277optimised for the broader context of the temporal and spatial variation of the environment in

278which the plant occurs (Laughlin and Messier 2015). One way in which plants can adapt to

279this variation is through active plastic responses to environmental cues that aim to maximise

280their fitness in dynamic environments (Sultan 2000, Morel‐ Journel et al. 2020). However, a

281plastic response will not necessarily allow the plant to express the optimal phenotype in all

282possible environments that the plant might encounter (Bongers et al. 2019, Douma et al.

2832019). Thus, plastic responses allow plants to scale between two extreme strategies: a Jack-

284of-all trades that is able to maintain fitness in unfavourable environments, versus a master-of-

285some that is able to maximise fitness in favourable environments (Richards et al. 2006).

286These strategies highlight that we must be cautious in assuming that a plant is expressing the

287optimal phenotype for the environment it is currently observed in, but rather consider the

288plant within the broader context of spatial and temporal variation in the environment.

289Understanding the role of phenotypic plasticity in a broad environmental context is essential

290to accurately predict plant population responses to climate change, both in the short and the

291long term (Nicotra et al. 2010, Valladares et al. 2014, Henn et al. 2018). Phenotypic plasticity

292is in itself a complex target of selection, and can play an important role in shaping

293evolutionary processes (Crispo 2008). On the one hand, phenotypic plasticity can dampen

294natural selection by allowing phenotypic divergence between populations without promoting

295genetic divergence. On the other hand, phenotypic plasticity may promote natural selection

296by allowing plant populations to adapt to, and persist in novel environments. However,

297phenotypic plasticity is often neglected in research on plant population responses to climate

298change (Matesanz and Ramírez‐ Valiente 2019), potentially because accurately measuring

299phenotypic plasticity requires elaborate experimental setups (Arnold et al. 2019). As a result,

12 300many outstanding questions regarding the role of phenotypic plasticity in plant population

301responses to climate change remain.

302 FSP modelling has proven to be an excellent tool to evaluate the adaptive value of

303phenotypic plasticity (Bongers et al. 2019). In FSP models, phenotypic plasticity can be

304mechanistically represented as a dose-response curve that describes the expression of a trait

305in response to a particular environmental cue (Evers et al. 2007). The calibration of these

306response curves relies on experimental studies to elucidate the different components of the

307plastic response, such as the type of response curve (Poorter et al. 2010), the different cues

308involved in the response (Pierik et al. 2013), as well as the location of signal perception and

309integration (Pantazopoulou et al. 2017). When such detailed experimental data is available,

310the shape of the response curve can be implemented in an evolutionary FSP model as a

311functional trait that is subject to selection by the (a)biotic environment (Bongers et al. 2018),

312and used to study how environmental variation drives selection for plastic responses. This

313goes beyond what is possible with experimental methods and is an important step towards

314understanding the role of phenotypic plasticity in the response of plant communities to the

315novel conditions caused by climate change. In particular, FSP models can shed light on the

316role of active plastic responses to cues that indicate environmental heterogeneity and mediate

317traits that vary on sub-individual levels. Some examples that are relevant in the light of

318climate change include shade-avoidance responses to light cues that mediate novel or

319changing competitive interactions, plasticity in root and leaf traits in response to temporal and

320spatial heterogeneity in the availability of nutrients and water, phenological responses to

321temperature changes, and defence responses to herbivore or pathogen attack (Nicotra et al.

3222010).

323Conclusions

324Understanding climate change responses of plant communities requires consideration of

13 325functional trait variation at the individual level in relation to the local (a)biotic environment.

326These trait-environment interactions determine the individual vital rates that drive population

327level eco-evolutionary dynamics, which ultimately shape plant communities. Here, I have

328outlined how evolutionary FSP modelling can help understand climate change responses of

329plant communities by taking a mechanistic modelling approach that integrates processes from

330plant physiology to community scales. Evolutionary FSP modelling is a versatile tool to study

331interactions between traits and fitness and how plant physiology drives eco-evolutionary

332processes, and to unravel the mechanistic basis of species interactions.

333Acknowledgements

334I would like to thank Frank Sterck, Jake Alexander, Lydian Boschman and three anonymous

335reviewers for their constructive and helpful feedback. This work was supported by ETH

336Zürich Postdoctoral Fellowship 19-2 FEL-72.

337Declaration of Competing Interest

338The author declares that has no competing financial or personal interests that could have

339influenced the content of this paper.

340References

341Adler, P. B., R. Salguero-Gómez, A. Compagnoni, J. S. Hsu, J. Ray-Mukherjee, C. Mbeau-Ache, and M. Franco. 342 2014. Functional traits explain variation in plant life history strategies. Proceedings of the National 343 Academy of Sciences 111:740-745. 344Adler, P. B., D. Smull, K. H. Beard, R. T. Choi, T. Furniss, A. Kulmatiski, J. M. Meiners, A. T. Tredennick, and K. 345 E. Veblen. 2018. Competition and coexistence in plant communities: intraspecific competition is 346 stronger than interspecific competition. Ecology Letters 21:1319-1329. 347Agrawal, A. A., J. A. Lau, and P. A. Hamback. 2006. Community heterogeneity and the evolution of interactions 348 between plants and insect herbivores. The Quarterly Review of Biology 81:349-376. 349Alexander, J. M., J. M. Diez, S. P. Hart, and J. M. Levine. 2016. When climate reshuffles competitors: a call for 350 experimental macroecology. Trends in Ecology & Evolution 31:831-841. 351Alexander, J. M., J. M. Diez, and J. M. Levine. 2015. Novel competitors shape species’ responses to climate 352 change. Nature 525:515. 353Anderson, J., and B. H. Song. 2020. Plant adaptation to climate change—Where are we? Journal of Systematics 354 and Evolution. 355Anderson, J. T., and S. M. Wadgymar. 2020. Climate change disrupts local adaptation and favours upslope 356 migration. Ecology Letters 23:181-192. 357Angert, A. L., L. G. Crozier, L. J. Rissler, S. E. Gilman, J. J. Tewksbury, and A. J. Chunco. 2011. Do species’ 358 traits predict recent shifts at expanding range edges? Ecology Letters 14:677-689. 359Arnold, P. A., L. E. Kruuk, and A. B. Nicotra. 2019. How to analyse plant phenotypic plasticity in response to a 360 changing climate. New Phytologist 222:1235-1241. 361Atkins, K., and J. Travis. 2010. Local adaptation and the evolution of species’ ranges under climate change. 362 Journal of Theoretical Biology 266:449-457.

14 363Bolnick, D. I., P. Amarasekare, M. S. Araújo, R. Bürger, J. M. Levine, M. Novak, V. H. Rudolf, S. J. Schreiber, 364 M. C. Urban, and D. A. Vasseur. 2011. Why intraspecific trait variation matters in community ecology. 365 Trends in Ecology & Evolution 26:183-192. 366Bongers, F. J. 2020. Functional-structural plant models to boost understanding of complementarity in light 367 capture and use in mixed-species forests. Basic and Applied Ecology. 368Bongers, F. J., J. C. Douma, Y. Iwasa, R. Pierik, J. B. Evers, and N. P. Anten. 2019. Variation in plastic 369 responses to light results from selection in different competitive environments—A game theoretical 370 approach using virtual plants. PLoS computational biology 15. 371Bongers, F. J., J. B. Evers, N. P. R. Anten, and R. Pierik. 2014. From shade avoidance responses to plant 372 performance at vegetation level: using virtual plant modelling as a tool. New Phytologist 204:268- 373 272. 374Bongers, F. J., R. Pierik, N. P. R. Anten, and J. B. Evers. 2018. Subtle variation in shade avoidance responses 375 may have profound consequences for plant competitiveness. Annals of Botany 121:863-873. 376Bornhofen, S., S. Barot, and C. Lattaud. 2011. The evolution of CSR life-history strategies in a plant model 377 with explicit physiology and architecture. Ecological Modelling 222:1-10. 378Braghiere, R. K., F. Gérard, J. B. Evers, C. Pradal, and L. Pagès. 2020. Simulating the effects of water 379 limitation on plant biomass using a 3D functional–structural plant model of shoot and root driven by 380 soil hydraulics. Annals of Botany 126:713-728. 381Briscoe Runquist, R. D., A. J. Gorton, J. B. Yoder, N. J. Deacon, J. J. Grossman, S. Kothari, M. P. Lyons, S. N. 382 Sheth, P. Tiffin, and D. A. Moeller. 2020. Context dependence of local adaptation to abiotic and biotic 383 environments: a quantitative and qualitative synthesis. The American Naturalist 195:412-431. 384Chen, T.-W., T. M. N. Nguyen, K. Kahlen, and H. Stützel. 2014. Quantification of the effects of architectural 385 traits on dry mass production and light interception of tomato canopy under different temperature 386 regimes using a dynamic functional–structural plant model. Journal of Experimental Botany 65:6399- 387 6410. 388Colbach, N. 2009. How to model and simulate the effects of cropping systems on population dynamics and 389 gene flow at the landscape level: example of oilseed rape volunteers and their role for co-existence of 390 GM and non-GM crops. Environmental Science and Pollution Research 16:348-360. 391Crispo, E. 2008. Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation 392 and gene flow. Journal of Evolutionary Biology 21:1460-1469. 393De Visser, J. A. G., and J. Krug. 2014. Empirical fitness landscapes and the predictability of evolution. Nature 394 Reviews Genetics 15:480-490. 395de Vries, J., J. B. Evers, M. Dicke, and E. H. Poelman. 2019. Ecological interactions shape the adaptive value of 396 plant defence: herbivore attack versus competition for light. Functional Ecology 33:129-138. 397de Vries, J., J. B. Evers, T. W. Kuyper, J. Ruijven, and L. Mommer. 2021. Mycorrhizal associations change root 398 functionality: a 3D modelling study on competitive interactions between plants for light and nutrients. 399 New Phytologist. 400de Vries, J., J. B. Evers, E. H. Poelman, and N. P. Anten. 2020. Simulation of optimal defence against 401 herbivores under resource limitation and competition using an evolutionary functional-structural plant 402 model. in silico Plants 2. 403de Vries, J., E. H. Poelman, N. P. Anten, and J. B. Evers. 2018. Elucidating the interaction between light 404 competition and herbivore feeding patterns using functional–structural plant modelling. Annals of 405 Botany 121:1019-1031. 406de Wit, M., W. Kegge, J. B. Evers, M. H. V. Eijk, P. Gankema, L. A. C. J. Voesenek, and R. Pierik. 2012. Plant 407 neighbor detection through touching leaf tips precedes phytochrome signals. Proceedings of the 408 National Academy of Sciences 109:14705-14710. 409Díaz, S., J. Kattge, J. H. Cornelissen, I. J. Wright, S. Lavorel, S. Dray, B. Reu, M. Kleyer, C. Wirth, and I. C. 410 Prentice. 2016. The global spectrum of plant form and function. Nature 529:167. 411Douma, J. C., J. de Vries, E. H. Poelman, M. Dicke, N. P. Anten, and J. B. Evers. 2019. Ecological significance 412 of light quality in optimizing plant defence. Plant, Cell & Environment 42:1065-1077. 413Drouet, J.-L., and L. Pagès. 2007. GRAAL-CN: A model of GRowth, Architecture and ALlocation for Carbon and 414 Nitrogen dynamics within whole plants formalised at the organ level. Ecological Modelling 206:231- 415 249. 416Dunbabin, V., Z. Rengel, and A. J. Diggle. 2004. Simulating form and function of root systems: efficiency of 417 nitrate uptake is dependent on root system architecture and the spatial and temporal variability of 418 nitrate supply. Functional Ecology 18:204-211. 419Evers, J., J. Vos, X. Yin, P. Romero, P. Van Der Putten, and P. Struik. 2010. Simulation of wheat growth and 420 development based on organ-level photosynthesis and assimilate allocation. Journal of Experimental 421 Botany 61:2203-2216. 422Evers, J. B., and L. Bastiaans. 2016. Quantifying the effect of crop spatial arrangement on weed suppression 423 using functional-structural plant modelling. Journal of plant research 129:339-351. 424Evers, J. B., V. Letort, M. Renton, and M. Kang. 2018. Computational botany: advancing plant science through 425 functional–structural plant modelling. Annals of Botany 121:767-772. 426Evers, J. B., J. Vos, M. Chelle, B. Andrieu, C. Fournier, and P. C. Struik. 2007. Simulating the effects of 427 localized red:far-red ratio on tillering in spring wheat (Triticum aestivum) using a three-dimensional 428 virtual plant model. New Phytologist 176:325-336. 429Falster, D. S., and M. Westoby. 2003. Plant height and evolutionary games. Trends in Ecology & Evolution 430 18:337-343. 431Faverjon, L., A. Escobar-Gutiérrez, I. Litrico, B. Julier, and G. Louarn. 2019. A generic individual-based model 432 can predict yield, nitrogen content, and species abundance in experimental grassland communities. 433 Journal of Experimental Botany.

15 434Fitzpatrick, S., J. Gerberich, J. Kronenberger, L. Angeloni, and W. Funk. 2015. Locally adapted traits 435 maintained in the face of high gene flow. Ecology Letters 18:37-47. 436Franks, S., and A. Weis. 2008. A change in climate causes rapid evolution of multiple life‐history traits and 437 their interactions in an annual plant. Journal of Evolutionary Biology 21:1321-1334. 438Franks, S. J., S. Sim, and A. E. Weis. 2007. Rapid evolution of flowering time by an annual plant in response to 439 a climate fluctuation. Proceedings of the National Academy of Sciences 104:1278-1282. 440Funk, J. L., J. E. Larson, G. M. Ames, B. J. Butterfield, J. Cavender‐Bares, J. Firn, D. C. Laughlin, A. E. Sutton‐ 441 Grier, L. Williams, and J. Wright. 2017. Revisiting the H oly G rail: using plant functional traits to 442 understand ecological processes. Biological Reviews 92:1156-1173. 443Garant, D., S. E. Forde, and A. P. Hendry. 2007. The multifarious effects of dispersal and gene flow on 444 contemporary adaptation. Functional Ecology 21:434-443. 445García‐Ramos, G., and M. Kirkpatrick. 1997. Genetic models of adaptation and gene flow in peripheral 446 populations. Evolution 51:21-28. 447Garin, G., C. Fournier, B. Andrieu, V. Houlès, C. Robert, and C. Pradal. 2014. A modelling framework to 448 simulate foliar fungal epidemics using functional–structural plant models. Annals of Botany 114:795- 449 812. 450Gauthier, M., R. Barillot, A. Schneider, C. Chambon, C. Fournier, C. Pradal, C. Robert, and B. Andrieu. 2020. A 451 functional structural model of grass development based on metabolic regulation and coordination 452 rules. Journal of Experimental Botany 71:5454-5468. 453Gavrilets, S. 2010. High-dimensional fitness landscapes and speciation. Evolution: the extended synthesis:45- 454 79. 455Griffith, T., and M. Watson. 2005. Stress avoidance in a common annual: reproductive timing is important for 456 local adaptation and geographic distribution. Journal of Evolutionary Biology 18:1601-1612. 457Haldane, J. B. S. 1930. A mathematical theory of natural and artificial selection.(Part VI, Isolation.). Pages 458 220-230 in Mathematical Proceedings of the Cambridge Philosophical Society. Cambridge University 459 Press. 460Hargreaves, A. L., R. M. Germain, M. Bontrager, J. Persi, and A. L. Angert. 2020. Local adaptation to biotic 461 interactions: A meta-analysis across latitudes. The American Naturalist 195:395-411. 462Hargreaves, A. L., K. E. Samis, and C. G. Eckert. 2014. Are species’ range limits simply niche limits writ large? 463 A review of transplant experiments beyond the range. The American Naturalist 183:157-173. 464Henn, J. J., V. Buzzard, B. J. Enquist, A. H. Halbritter, K. Klanderud, B. S. Maitner, S. T. Michaletz, C. Pötsch, 465 L. Seltzer, and R. J. Telford. 2018. Intraspecific trait variation and phenotypic plasticity mediate alpine 466 plant species response to climate change. Frontiers in plant science 9:1548. 467HilleRisLambers, J., M. A. Harsch, A. K. Ettinger, K. R. Ford, and E. J. Theobald. 2013. How will biotic 468 interactions influence climate change–induced range shifts? Annals of the New York Academy of 469 Sciences 1297:112-125. 470Hitz, T., M. Henke, S. Graeff-Hönninger, and S. Munz. 2019. Three-dimensional simulation of light spectrum 471 and intensity within an LED growth chamber. Computers and Electronics in Agriculture 156:540-548. 472Hodge, A., and A. H. Fitter. 2013. Microbial mediation of plant competition and community structure. 473 Functional Ecology 27:865-875. 474Kraft, N. J., O. Godoy, and J. M. Levine. 2015. Plant functional traits and the multidimensional nature of 475 species coexistence. Proceedings of the National Academy of Sciences 112:797-802. 476Kunstler, G., D. Falster, D. A. Coomes, F. Hui, R. M. Kooyman, D. C. Laughlin, L. Poorter, M. Vanderwel, G. 477 Vieilledent, and S. J. Wright. 2016. Plant functional traits have globally consistent effects on 478 competition. Nature 529:204-207. 479Laughlin, D. C. 2014. The intrinsic dimensionality of plant traits and its relevance to community assembly. 480 Journal of Ecology 102:186-193. 481Laughlin, D. C., J. R. Gremer, P. B. Adler, R. M. Mitchell, and M. M. Moore. 2020. The net effect of functional 482 traits on fitness. Trends in Ecology & Evolution. 483Laughlin, D. C., and J. Messier. 2015. Fitness of multidimensional phenotypes in dynamic adaptive landscapes. 484 Trends in Ecology & Evolution 30:487-496. 485Lavorel, S., and E. Garnier. 2002. Predicting changes in community composition and ecosystem functioning 486 from plant traits: revisiting the Holy Grail. Functional Ecology 16:545-556. 487Levine, J. M., J. Bascompte, P. B. Adler, and S. Allesina. 2017. Beyond pairwise mechanisms of species 488 coexistence in complex communities. Nature 546:56-64. 489Louarn, G., and L. Faverjon. 2018. A generic individual-based model to simulate morphogenesis, C–N 490 acquisition and population dynamics in contrasting forage legumes. Annals of Botany 121:875-896. 491Louarn, G., and Y. Song. 2020. Two decades of functional–structural plant modelling: now addressing 492 fundamental questions in systems biology and predictive ecology. Annals of Botany 126:501-509. 493Louthan, A. M., D. F. Doak, and A. L. Angert. 2015. Where and when do species interactions set range limits? 494 Trends in Ecology & Evolution 30:780-792. 495Lowe, W. H., R. P. Kovach, and F. W. Allendorf. 2017. Population genetics and demography unite ecology and 496 evolution. Trends in Ecology & Evolution 32:141-152. 497Lustenhouwer, N., R. A. Wilschut, J. L. Williams, W. H. van der Putten, and J. M. Levine. 2018. Rapid evolution 498 of phenology during range expansion with recent climate change. Global Change Biology 24:e534- 499 e544. 500MacColl, A. D. 2011. The ecological causes of evolution. Trends in Ecology & Evolution 26:514-522. 501Matesanz, S., and J. A. Ramírez‐Valiente. 2019. A review and meta‐analysis of intraspecific differences in 502 phenotypic plasticity: implications to forecast plant responses to climate change. Global Ecology and 503 Biogeography 28:1682-1694.

16 504McGill, B. J., B. J. Enquist, E. Weiher, and M. Westoby. 2006. Rebuilding community ecology from functional 505 traits. Trends in Ecology & Evolution 21:178-185. 506McNickle, G. G., and R. Dybzinski. 2013. Game theory and plant ecology. Ecology Letters 16:545-555. 507Mordecai, E. A. 2011. Pathogen impacts on plant communities: unifying theory, concepts, and empirical work. 508 Ecological Monographs 81:429-441. 509Morel‐Journel, T., V. Thuillier, F. Pennekamp, E. Laurent, D. Legrand, A. S. Chaine, and N. Schtickzelle. 2020. 510 A multidimensional approach to the expression of phenotypic plasticity. Functional Ecology. 511Nicotra, A. B., O. K. Atkin, S. P. Bonser, A. M. Davidson, E. Finnegan, U. Mathesius, P. Poot, M. D. Purugganan, 512 C. L. Richards, and F. Valladares. 2010. Plant phenotypic plasticity in a changing climate. Trends in 513 Plant Science 15:684-692. 514Oddou-Muratorio, S., H. Davi, and F. Lefèvre. 2020. Integrating evolutionary, demographic and 515 ecophysiological processes to predict the adaptive dynamics of forest tree populations under global 516 change. Tree Genetics & Genomes 16:1-22. 517Pagel, J., and F. M. Schurr. 2012. Forecasting species ranges by statistical estimation of ecological niches and 518 spatial population dynamics. Global Ecology and Biogeography 21:293-304. 519Pantazopoulou, C. K., F. J. Bongers, J. J. Küpers, E. Reinen, D. Das, J. B. Evers, N. P. R. Anten, and R. Pierik. 520 2017. Neighbor detection at the leaf tip adaptively regulates upward leaf movement through spatial 521 auxin dynamics. Proceedings of the National Academy of Sciences 114:7450-7455. 522Pierik, R., L. Mommer, and L. A. C. J. Voesenek. 2013. Molecular mechanisms of plant competition: neighbour 523 detection and response strategies. Functional Ecology 27:841-853. 524Poorter, H., Ü. Niinemets, A. Walter, F. Fiorani, and U. Schurr. 2010. A method to construct dose–response 525 curves for a wide range of environmental factors and plant traits by means of a meta-analysis of 526 phenotypic data. Journal of Experimental Botany 61:2043-2055. 527Post, E. 2013. Ecology of climate change: the importance of biotic interactions. Princeton University Press. 528Postma, J. A., and J. P. Lynch. 2012. Complementarity in root architecture for nutrient uptake in ancient 529 maize/bean and maize/bean/squash polycultures. Annals of Botany 110:521-534. 530Postma, J. A., U. Schurr, and F. Fiorani. 2014. Dynamic root growth and architecture responses to limiting 531 nutrient availability: linking physiological models and experimentation. Biotechnology Advances 532 32:53-65. 533Renton, M., J. Hanan, and K. Burrage. 2005. Using the canonical modelling approach to simplify the simulation 534 of function in functional–structural plant models. New Phytologist 166:845-857. 535Renton, M., and P. Poot. 2014. Simulation of the evolution of root water foraging strategies in dry and shallow 536 soils. Annals of Botany 114:763-778. 537Richards, C. L., O. Bossdorf, N. Z. Muth, J. Gurevitch, and M. Pigliucci. 2006. Jack of all trades, master of 538 some? On the role of phenotypic plasticity in plant invasions. Ecology Letters 9:981-993. 539Rieseberg, L. H., and J. Burke. 2001. A genic view of species integration. Journal of Evolutionary Biology 540 14:883-886. 541Robert, C., C. Fournier, B. Andrieu, and B. Ney. 2008. Coupling a 3D virtual wheat (Triticum aestivum) plant 542 model with a Septoria tritici epidemic model (Septo3D): a new approach to investigate plant– 543 pathogen interactions linked to canopy architecture. Functional Plant Biology 35:997-1013. 544Sargent, R. D., and D. D. Ackerly. 2008. Plant–pollinator interactions and the assembly of plant communities. 545 Trends in Ecology & Evolution 23:123-130. 546Schmidt, D., and K. Kahlen. 2018. Towards More Realistic Leaf Shapes in functional-structural plant models. 547 Symmetry 10:278. 548Schnepf, A., D. Leitner, P. Schweiger, P. Scholl, and J. Jansa. 2016. L-System model for the growth of 549 arbuscular mycorrhizal fungi, both within and outside of their host roots. Journal of The Royal Society 550 Interface 13:20160129. 551Seidl, R., D. Thom, M. Kautz, D. Martin-Benito, M. Peltoniemi, G. Vacchiano, J. Wild, D. Ascoli, M. Petr, and J. 552 Honkaniemi. 2017. Forest disturbances under climate change. Nature Climate Change 7:395-402. 553Slatkin, M. 1987. Gene flow and the geographic structure of natural populations. Science 236:787-792. 554Sterck, F., L. Markesteijn, F. Schieving, and L. Poorter. 2011. Functional traits determine trade-offs and niches 555 in a tropical forest community. Proceedings of the National Academy of Sciences 108:20627-20632. 556Sterck, F. J., and F. Schieving. 2007. 3‐D growth patterns of trees: effects of carbon economy, meristem 557 activity, and selection. Ecological Monographs 77:405-420. 558Streit, K., C. Bahr, J. B. Evers, M. Renton, G. Syme, D. MacDonald, B. Fulton, and J. Piantadosi. 2017. 559 Simulation of the progression of yellow spot on wheat using a functional-structural plant model 560 (FSPM): Model concepts. Pages 271-277 in 22nd International Congress on Modelling and Simulation 561 (MODSIM2017). 562Sultan, S. E. 2000. Phenotypic plasticity for plant development, function and life history. Trends in Plant 563 Science 5:537-542. 564Svensson, E., and R. Calsbeek. 2012. The adaptive landscape in evolutionary biology. Oxford University Press. 565Tallmon, D. A., G. Luikart, and R. S. Waples. 2004. The alluring simplicity and complex reality of genetic 566 rescue. Trends in Ecology & Evolution 19:489-496. 567Urban, M. C., G. Bocedi, A. P. Hendry, J.-B. Mihoub, G. Pe’er, A. Singer, J. Bridle, L. Crozier, L. De Meester, 568 and W. Godsoe. 2016. Improving the forecast for biodiversity under climate change. Science 569 353:aad8466. 570Valladares, F., S. Matesanz, F. Guilhaumon, M. B. Araújo, L. Balaguer, M. Benito‐Garzón, W. Cornwell, E. 571 Gianoli, M. van Kleunen, and D. E. Naya. 2014. The effects of phenotypic plasticity and local 572 adaptation on forecasts of species range shifts under climate change. Ecology Letters 17:1351-1364.

17 573van der Plas, F., T. Schröder-Georgi, A. Weigelt, K. Barry, S. Meyer, A. Alzate, R. L. Barnard, N. Buchmann, H. 574 de Kroon, and A. Ebeling. 2020. Plant traits alone are poor predictors of ecosystem properties and 575 long-term ecosystem functioning. Nature ecology & evolution 4:1602-1611. 576Weiner, J. 1990. Asymmetric competition in plant populations. Trends in Ecology & Evolution 5:360-364. 577Williams, J. W., and S. T. Jackson. 2007. Novel climates, no‐analog communities, and ecological surprises. 578 Frontiers in Ecology and the Environment 5:475-482. 579Wright, I. J., P. B. Reich, M. Westoby, D. D. Ackerly, Z. Baruch, F. Bongers, J. Cavender-Bares, T. Chapin, J. H. 580 Cornelissen, and M. Diemer. 2004. The worldwide leaf economics spectrum. Nature 428:821. 581Yang, J., M. Cao, and N. G. Swenson. 2018. Why functional traits do not predict tree demographic rates. 582 Trends in Ecology & Evolution 33:326-336. 583Yoshinaka, K., H. Nagashima, Y. Yanagita, and K. Hikosaka. 2018. The role of biomass allocation between 584 lamina and petioles in a game of light competition in a dense stand of an annual plant. Annals of 585 Botany 121:1055-1064. 586Zakharova, L., K. Meyer, and M. Seifan. 2019. Trait-based modelling in ecology: A review of two decades of 587 research. Ecological Modelling 407:108703. 588Zhang, B., and D. L. DeAngelis. 2020. An overview of agent-based models in plant biology and ecology. Annals 589 of Botany 126:539-557. 590Zhou, X.-R., A. Schnepf, J. Vanderborght, D. Leitner, A. Lacointe, H. Vereecken, and G. Lobet. 2020. 591 CPlantBox, a whole-plant modelling framework for the simulation of water-and carbon-related 592 processes. in silico Plants 2:diaa001. 593Züst, T., and A. A. Agrawal. 2017. Trade-offs between plant growth and defense against insect herbivory: an 594 emerging mechanistic synthesis. Annual review of plant biology 68:513-534.

595

596Fig 1. A visual summary of the processes and scale of evolutionary FSP modelling. The FSP

597model simulates the morphology, physiology and phenology of individually distinct plants in

598relation to their (a)biotic environment, which shapes individual vital rates (growth,

599reproduction and survival). The FSP model is coupled to the evolutionary model through a)

600one or more heritable parameters (e.g. genes, traits) that serve as input to the FSP model and

601are subject to selection, gene flow and genetic, and b) the fitness components that are the

602output of the FSP model and drive selection, gene flow and genetic drift. These population

603level processes in turn determine the community-level variation that determine what

604genotypes are present in the population, as well as the biotic interactions that these

605individuals experience. This coupling requires the model to balance the high spatial and

606temporal resolution required to simulate detailed physiological processes and the large extent

607required to simulate demographic and evolutionary processes that shape populations and

608communities.

18